Time Series-based Forecasting of New Energy Vehicle Sales:Taking BYD as an Example
The development of new energy vehicles plays a critical role in achieving the"dual carbon"goals.Accurate sales forecasting is of great importance for policy-making and corporate growth.BYD's new energy vehicle is used as the research object,and seasonal autoregressive integrated moving average(SARIMA)and long short term memory(LSTM)models are constructed based on their historical sales data to forecast future sales.To improve model performance,an ARIMA-LSTM(autoregressive integrated moving average and long short term memory)hybrid model is created.Sales data is decomposed into linear and nonlinear parts.The ARIMA model is used to forecast trends,while the LSTM model is applied to predict residuals and other nonlinear data.The final results from both models are combined.This hybrid model is applied to forecast domestic new energy vehicle sales,and its accuracy is 90.96%,showing significant improvement over single-model predictions.
automobile sales forecastingseasonal autoregressive integrated moving average(SARIMA)neural networksnew energy vehicles
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